Systematic Literature Review

Bibliometrix Analysis using R

• Bibliometrix (https://www.bibliometrix.org/) allows R users to import a bibliography database generated using SCOPUS and Web of Science stored either as a Bibtex (.bib) or Plain Text (.txt) file.

• The package has simple functions which allows for descriptive analyses as shown in table-1 to table-3.

• The analysis can also be easily visualised as shown in figure-1.

library(bibliometrix) #load the package
library(pander)#other required packages
library(knitr)
library(kableExtra)
library(ggplot2)
library(bibliometrixData)
#use scopuscollection data from the package
# Manuscripts including the term "bibliometrics" in the title.
# Period: 1975 - 2017
# Database: SCOPUS
# Format: bibtex
data("scopusCollection")
file1=data("scopusCollection")
#M=convert2df(file="insert filename",format="bibtex",dbsource = "scopus")#convert the data to data frame

#scopusCollection=convert2df(file="scopus.bib",dbsource = "scopus",format="bibtex")

Descriptive Analysis

Productive Authors

--------------------------------------------
          Description              Results  
-------------------------------- -----------
  MAIN INFORMATION ABOUT DATA               

            Timespan              1975:2017 

 Sources (Journals, Books, etc)      280    

           Documents                 487    

 Average years from publication     13.6    

     Average citations per          10.36   
           documents                        

 Average citations per year per    0.6601   
              doc                           

           References               12245   

         DOCUMENT TYPES                     

            article                  417    

              book                   12     

           conference                58     

       DOCUMENT CONTENTS                    

       Keywords Plus (ID)           1436    

     Author's Keywords (DE)          722    

            AUTHORS                         

            Authors                  949    

       Author Appearances           1187    

   Authors of single-authored        162    
           documents                        

   Authors of multi-authored         787    
           documents                        

     AUTHORS COLLABORATION                  

   Single-authored documents         184    

      Documents per Author          0.513   

      Authors per Document          1.95    

    Co-Authors per Documents        2.44    

      Collaboration Index            2.6    

                                            
--------------------------------------------

Table: Summary Information

Most cited papers

-----------------------------------------------------------------
    Authors      Articles    Authors     Articles Fractionalized 
--------------- ---------- ------------ -------------------------
  BORNMANN L        13      BORNMANN L            6.75           

  KOSTOFF RN        8        HOLDEN G             4.25           

   GLNZEL W         7        WHITE HD             4.00           

   HOLDEN G         7         MARX W              3.42           

    MARX W          7       ATKINSON R            3.00           

    HUANG L         5           NA                3.00           

  HUMENIK JA        5        GLNZEL W             2.67           

  LARIVIRE V        5        KIRBY A              2.50           

 LEYDESDORFF L      5       PERITZ BC             2.50           

    ZHANG X         5        SMITH DR             2.50           
-----------------------------------------------------------------

Table: Most Productive Authors

Information Plots

Summary Plot-1 (Most Porductive Authors)

Summary Plot-2 (Most Productive Countries)

Summary Plot-3 (Annual Scientific Production)

Summary Plot-4 (Average Article Citation)

• A graph for author statistics over time can also be produced.

• Figure-1 shows a graph of top 10 authors over time. The information from these plots can be easily extracted to summarise them in a table.

• The package also facilitates various network analysis like, co-citation analysis, coupling analysis, collaboration analysis or co-occurrence analysis. Figure-2 shows a key word co-occurrence plot

• Bibliometrix provides another useful function to plot a Sankey diagram to visualise multiple attributes at the same time. For example, figure-9 provides a three fields plot for Author, Author Keywords and Cited References.

Co-word Analysis

• Analysis of the conceptual structure among the articles analysed.
• Bibliomentrix can conduct a co-word analysis to map the conceptual structure of a framework using the word co-occurrences in a bibliographic database.
• The analysis in Figure-2 is conducted using the Correspondence Analysis and K-Means clustering using Author’s keywords. This analysis includes Natural Language Processing and is conducted without stemming.

Author collaboration network

Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
Warning: ggrepel: 5 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 11 unlabeled data points (too many overlaps). Consider increasing max.overlaps

Thematic Map

Co-word analysis draws clusters of keywords. They are considered as themes, whose density and centrality can be used in classifying themes and mapping in a two-dimensional diagram.

Thematic map is a very intuitive plot and we can analyze themes according to the quadrant in which they are placed: (1) upper-right quadrant: motor-themes; (2) lower-right quadrant: basic themes; (3) lower-left quadrant: emerging or disappearing themes; (4) upper-left quadrant: very specialized/niche themes.

#Map2=thematicEvolution(M3,field="ID",n=1000,stemming=FALSE,repel=TRUE,years=2000)
Map=thematicMap(M, field = "ID", n = 1000, minfreq = 5,stemming = FALSE, size = 0.5, n.labels=4, repel = TRUE)
plot(Map$map)

There is a gui too!

This concludes the example. There are various online sources to take this further